Machine learning for ISIS Controls

Machine learning methods have been demonstrated significant promise when applied in the context of accelerator controls. This work highlights the ongoing efforts using ML methods to improve and optimise the operation of the accelerator. We present current applications in anomaly detection, optimisation and digital twinning. As well as control applications the machine learning operations (MLOps) work required for rapid prototyping and reliable and repeatable deployment is presented. Finally, we discuss the current and future impact of ML onthe ISIS Control system.